4.4 Article

Dynamic Stress of Subgrade Bed Layers Subjected to Train Vehicles with Large Axle Loads

Journal

SHOCK AND VIBRATION
Volume 2018, Issue -, Pages -

Publisher

HINDAWI LTD
DOI: 10.1155/2018/2916096

Keywords

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Funding

  1. National Natural Science Foundation of China [51678572, 51709284, 51878666]
  2. Postdoctoral Science Foundation of Hunan Communications Research Institute Co., Ltd.

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The dynamic responses of subgrade bed layers are the key factors affecting the service performance of a heavy-haul railway. A 3D train-track-subgrade interaction finite element (FE) model was constructed using the ABAQUS code, where different vertical irregular track spectra were simulated by modifying the vertical node coordinates of the FE mesh of the rail. Then, the dynamic stresses in the subgrade bed layers subjected to heavy-haul trains were studied in detail. The results showed the following: (1) the transverse distribution of the dynamic stress transformed from a bimodal pattern to a unimodal pattern with increasing depth; (2) the pass of adjacent bogies of adjacent carriages can be simplified once loaded on the subgrade since the dynamic stresses are maintained around the peak value during the pass of the adjacent bogies; (3) the dynamic stress at the bottom of the subgrade bed surface layer was more sensitive to the train axle load compared with that at the subgrade surface because the dynamic stresses induced by the two rails were gradually overlaid with increasing depth; (4) the maximum dynamic stress at the subgrade bed bottom was reduced by approximately 70% compared with that at the subgrade surface; (5) the vertical track irregularities intensified the vertical excitation between the train vehicle wheels and rails, and the maximum dynamic stress at the subgrade surface under the action of the irregular heavy-haul track spectrum increased by 23% compared with the smooth rail condition; and (6) the possible maximum dynamic stress (sigma(dm)) at the subgrade surface under the action of irregular track spectra can be predicted using the triple standard deviation principle of a normally distributed random variable, i.e., sigma(dm) = + 3 sigma (where and sigma are the expectation and standard deviation of sigma(dm), respectively).

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